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dS)yaLSupport for collections of mapped entities.

The collections package supplies the machinery used to inform the ORM of
collection membership changes.  An instrumentation via decoration approach is
used, allowing arbitrary types (including built-ins) to be used as entity
collections without requiring inheritance from a base class.

Instrumentation decoration relays membership change events to the
:class:`.CollectionAttributeImpl` that is currently managing the collection.
The decorators observe function call arguments and return values, tracking
entities entering or leaving the collection.  Two decorator approaches are
provided.  One is a bundle of generic decorators that map function arguments
and return values to events::

  from sqlalchemy.orm.collections import collection


  class MyClass:
      # ...

      @collection.adds(1)
      def store(self, item):
          self.data.append(item)

      @collection.removes_return()
      def pop(self):
          return self.data.pop()

The second approach is a bundle of targeted decorators that wrap appropriate
append and remove notifiers around the mutation methods present in the
standard Python ``list``, ``set`` and ``dict`` interfaces.  These could be
specified in terms of generic decorator recipes, but are instead hand-tooled
for increased efficiency.  The targeted decorators occasionally implement
adapter-like behavior, such as mapping bulk-set methods (``extend``,
``update``, ``__setslice__``, etc.) into the series of atomic mutation events
that the ORM requires.

The targeted decorators are used internally for automatic instrumentation of
entity collection classes.  Every collection class goes through a
transformation process roughly like so:

1. If the class is a built-in, substitute a trivial sub-class
2. Is this class already instrumented?
3. Add in generic decorators
4. Sniff out the collection interface through duck-typing
5. Add targeted decoration to any undecorated interface method

This process modifies the class at runtime, decorating methods and adding some
bookkeeping properties.  This isn't possible (or desirable) for built-in
classes like ``list``, so trivial sub-classes are substituted to hold
decoration::

  class InstrumentedList(list):
      pass

Collection classes can be specified in ``relationship(collection_class=)`` as
types or a function that returns an instance.  Collection classes are
inspected and instrumented during the mapper compilation phase.  The
collection_class callable will be executed once to produce a specimen
instance, and the type of that specimen will be instrumented.  Functions that
return built-in types like ``lists`` will be adapted to produce instrumented
instances.

When extending a known type like ``list``, additional decorations are not
generally not needed.  Odds are, the extension method will delegate to a
method that's already instrumented.  For example::

  class QueueIsh(list):
      def push(self, item):
          self.append(item)

      def shift(self):
          return self.pop(0)

There's no need to decorate these methods.  ``append`` and ``pop`` are already
instrumented as part of the ``list`` interface.  Decorating them would fire
duplicate events, which should be avoided.

The targeted decoration tries not to rely on other methods in the underlying
collection class, but some are unavoidable.  Many depend on 'read' methods
being present to properly instrument a 'write', for example, ``__setitem__``
needs ``__getitem__``.  "Bulk" methods like ``update`` and ``extend`` may also
reimplemented in terms of atomic appends and removes, so the ``extend``
decoration will actually perform many ``append`` operations and not call the
underlying method at all.

Tight control over bulk operation and the firing of events is also possible by
implementing the instrumentation internally in your methods.  The basic
instrumentation package works under the general assumption that collection
mutation will not raise unusual exceptions.  If you want to closely
orchestrate append and remove events with exception management, internal
instrumentation may be the answer.  Within your method,
``collection_adapter(self)`` will retrieve an object that you can use for
explicit control over triggering append and remove events.

The owning object and :class:`.CollectionAttributeImpl` are also reachable
through the adapter, allowing for some very sophisticated behavior.

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    arguments.  They are not written with parens::

        @collection.appender
        def append(self, append): ...

    The recipe decorators all require parens, even those that take no
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            def add(self, append): ...


            # or, equivalently
            @collection.appender
            @collection.adds(1)
            def add(self, append): ...


            # for mapping type, an 'append' may kick out a previous value
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            def add(self, entity):
                key = some_key_func(entity)
                previous = None
                if key in self:
                    previous = self[key]
                self[key] = entity
                return previous

        If the value to append is not allowed in the collection, you may
        raise an exception.  Something to remember is that the appender
        will be called for each object mapped by a database query.  If the
        database contains rows that violate your collection semantics, you
        will need to get creative to fix the problem, as access via the
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            def zap(self, entity): ...


            # or, equivalently
            @collection.remover
            @collection.removes_return()
            def zap(self): ...

        If the value to remove is not present in the collection, you may
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        decoration from wrapping your implementation::

            # normally an 'extend' method on a list-like class would be
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        of values for the ORM's use.

        The default converter implementation will use duck-typing to do the
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        of dictionary values, and other types will simply be iterated::

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        If the duck-typing of the object does not match the type of this
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